Combinatorial drug therapy is useful for combating complex and refractory diseases such as acquired immune deficiency syndrome (AIDS), cancer and Type 2 diabetes mellitus (T2DM). Combinations of drugs work synergistically to improve therapeutic efficacy or work antagonistically to alleviate the risk of adverse drug reactions. For example, the combined use of aspirin and dipyridamole has been shown to be more beneficial and safer than using either of the drugs alone for secondary prevention of stroke. Despite the increasing number of drug combinations used, a significant challenge remains in discovering beneficial drug-drug combinations (DDCs) in a scalable manner. Most combinations are found in a clinical setting through experience or are experimentally derived by dose-response curves for a pair of drugs against a protein target. Recently, sources of large-scale data on drugs have been created that include detailed chemical, pharmacological, and pharmaceutical data along with sequence, structure, and pathway information about drug targets.
While the identification of novel DDCs is expected to contribute to the development of combinatorial drug therapy, existing studies are based on in-vitro experimental data, data collected from a limited number of participants in clinical trials or data from limited well-known drug combinations extracted from the Food and Drug Administration (FDA) orange book. Spontaneous reporting systems (SRSs) routinely collect drug-induced adverse drug events (ADEs) from patients on single medication or complex combinations of medications, which provide an opportunity to discover unexpected beneficial drug combinations for ADE reduction. Researchers have used SRSs to identify drug combinations that lead to unanticipated harmful adverse events, hereinafter referred to as drug-drug interactions (DDIs), and developed methodologies to effectively mine this database. For example, one researcher implemented a three-way disproportionality measure to identify suspected DDIs, and evaluated the method using empirical examples among the 20 highest predictions. Another researcher used association rule mining to identify multi-item ADE associations from the FDA adverse event reporting systems (FAERS), and 4% of results were characterized and validated as DDIs by an expert. Another attempt at utilizing FAERS reports mined these reports for side-effect profiles related to glucose homeostasis and uncovered a novel interaction between pravastatin and paroxetine that causes a potentially hazardous increase in blood glucose levels.
In theory, both adverse drug combinations (DDIs) and beneficial drug combinations (DDCs) resulting in ADE reduction should be obtainable from SRSs. A proposed method to identify DDCs based on FAERS utilized difference-in-differences estimators to look for drug pairs in which a second drug, i.e., Drug B, when taken with a first drug, i.e., Drug A, could reduce reports of adverse events from patients taking Drug A. For example, the combined therapy of rosiglitazone and exenatide reduced the reported incidences of myocardial infarction associated with the use of rosiglitazone alone.
A known issue with SRS data is selection biases resulting from the nonrandom selection of subjects that are exposed to the drug and that experience adverse events. This selection bias could lead to many false positive associations between the drug and the ADR when a causative covariate, e.g., a patient's disease state or other medications, is not taken into account. In order to alleviate such bias, an extensive post-hoc analysis is conducted based on stratification of the data on predefined covariates such as the use of metformin and Type 2 diabetes. However, such stratification requires significant domain knowledge and sometimes enumeration of important covariates, which is intractable for any large-scale analysis. Propensity score matching (PSM) is the most developed and popular strategy for causal analysis in observational studies that yield an unbiased estimate of treatment effects. The PSM for single drug-ADR detection has been successfully applied, and results showed that PSM can reduce selection bias associated with drugs reported in the case reports, decreasing the false positive associations.